Business Understanding:

• #### Business Goal: Time prediction in the world of food delivery

• #### Client Ref:

• #### Business Goal Affecting Features:

   Features that can affect food delivery estimation,
       -ID
       -Delivery_person_ID
       -Delivery_person_Age
       -Delivery_person_Ratings 
       -Restaurant_latitude
       -Restaurant_longitude
       -Delivery_location_latitude
       -Delivery_location_longitude 
       -Type_of_order
       -Type_of_vehicle

TOC

• 0. Data
• 1. Data Validation
• 2. EDA
• 3. Missing Values & Outliers
• 4. Predictive Modeling
  • 4.1 X & y
  • 4.2 Feature Engineering
  • 4.3 Train-Test Split

0. Data

Back to Top

• To do analysis on this, we need to collect multiple data for the above features
• For this project , We had taken open source dataset

DatasetRef: Open Sources

• Loading DataSet

# Base Python Libraries - Data Manipulation

import pandas as pd
import numpy as np

import warnings
warnings.filterwarnings("ignore")

# Loading Data using pandas methods, with variable/object name 'raw'

raw = pd.read_excel(r"C:\Users\vinit\OneDrive\Pictures\Data.xlsx.xlsx", sheet_name='Data')

raw

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	4607	INDORES13DEL02	37	4.9	22.745049	75.892471	22.765049	75.912471	Snack	motorcycle	24
1	B379	BANGRES18DEL02	34	4.5	12.913041	77.683237	13.043041	77.813237	Snack	scooter	33
2	5D6D	BANGRES19DEL01	23	4.4	12.914264	77.678400	12.924264	77.688400	Drinks	motorcycle	26
3	7A6A	COIMBRES13DEL02	38	4.7	11.003669	76.976494	11.053669	77.026494	Buffet	motorcycle	21
4	70A2	CHENRES12DEL01	32	4.6	12.972793	80.249982	13.012793	80.289982	Snack	scooter	30
...	...	...	...	...	...	...	...	...	...	...	...
45588	7C09	JAPRES04DEL01	30	4.8	26.902328	75.794257	26.912328	75.804257	Meal	motorcycle	32
45589	D641	AGRRES16DEL01	21	4.6	0.000000	0.000000	0.070000	0.070000	Buffet	motorcycle	36
45590	4F8D	CHENRES08DEL03	30	4.9	13.022394	80.242439	13.052394	80.272439	Drinks	scooter	16
45591	5EEE	COIMBRES11DEL01	20	4.7	11.001753	76.986241	11.041753	77.026241	Snack	motorcycle	26
45592	5FB2	RANCHIRES09DEL02	23	4.9	23.351058	85.325731	23.431058	85.405731	Snack	scooter	36

45593 rows × 11 columns

• We have a total of 45593 records/rows & 11 features/columns

• Columns Info: Coulmn name|Description ----------------|---------- ID |order ID number
  Delivery_person_ID|ID number of the delivery partner Delivery_person_Age|Age of the delivery partner Delivery_person_Ratings|ratings of the delivery partner based on past deliveries Restaurant_latitude|The latitude of the restaurant Restaurant_longitude|The longitude of the restaurant Delivery_location_latitude|The latitude of the delivery location Delivery_location_longitude|The longitude of the delivery location Type_of_order|The type of food ordered by the customer Type_of_vehicle|The type of vehicle delivery partner rides Time_taken(min)|The time taken by the delivery partner to deliver the food order

• Basic checks of data

raw.shape

(45593, 11)

print("First five rows of Dataset:")
display(raw.head())

print("Last five rows of Dataset:")
raw.tail()

First five rows of Dataset:

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	4607	INDORES13DEL02	37	4.9	22.745049	75.892471	22.765049	75.912471	Snack	motorcycle	24
1	B379	BANGRES18DEL02	34	4.5	12.913041	77.683237	13.043041	77.813237	Snack	scooter	33
2	5D6D	BANGRES19DEL01	23	4.4	12.914264	77.678400	12.924264	77.688400	Drinks	motorcycle	26
3	7A6A	COIMBRES13DEL02	38	4.7	11.003669	76.976494	11.053669	77.026494	Buffet	motorcycle	21
4	70A2	CHENRES12DEL01	32	4.6	12.972793	80.249982	13.012793	80.289982	Snack	scooter	30

Last five rows of Dataset:

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
45588	7C09	JAPRES04DEL01	30	4.8	26.902328	75.794257	26.912328	75.804257	Meal	motorcycle	32
45589	D641	AGRRES16DEL01	21	4.6	0.000000	0.000000	0.070000	0.070000	Buffet	motorcycle	36
45590	4F8D	CHENRES08DEL03	30	4.9	13.022394	80.242439	13.052394	80.272439	Drinks	scooter	16
45591	5EEE	COIMBRES11DEL01	20	4.7	11.001753	76.986241	11.041753	77.026241	Snack	motorcycle	26
45592	5FB2	RANCHIRES09DEL02	23	4.9	23.351058	85.325731	23.431058	85.405731	Snack	scooter	36

print("Column data Check (Null Values & Data Types):")
print()
raw.info()

Column data Check (Null Values & Data Types):

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45593 entries, 0 to 45592
Data columns (total 11 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   ID                           45593 non-null  object 
 1   Delivery_person_ID           45593 non-null  object 
 2   Delivery_person_Age          45593 non-null  int64  
 3   Delivery_person_Ratings      45593 non-null  float64
 4   Restaurant_latitude          45593 non-null  float64
 5   Restaurant_longitude         45593 non-null  float64
 6   Delivery_location_latitude   45593 non-null  float64
 7   Delivery_location_longitude  45593 non-null  float64
 8   Type_of_order                45593 non-null  object 
 9   Type_of_vehicle              45593 non-null  object 
 10  Time_taken(min)              45593 non-null  int64  
dtypes: float64(5), int64(2), object(4)
memory usage: 3.8+ MB

• Some of the column data which is numeric , is in object data type
• Need to take care of this columns data in Data Validation

print("Missing Values Count of Each Column:")
print()
raw.isnull().sum()

Missing Values Count of Each Column:

ID                             0
Delivery_person_ID             0
Delivery_person_Age            0
Delivery_person_Ratings        0
Restaurant_latitude            0
Restaurant_longitude           0
Delivery_location_latitude     0
Delivery_location_longitude    0
Type_of_order                  0
Type_of_vehicle                0
Time_taken(min)                0
dtype: int64

1. Data Validation & Cleaning

Back to Top

• Duplicated Rows Data

# As python is case sensitive , converting object data type columns into lower cases

for col in raw.columns:
    if raw[col].dtype=='object':
        raw[col] = raw[col].str.lower()

raw.head(2)

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	NaN	indores13del02	37	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle	24
1	b379	bangres18del02	34	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter	33

# Duplicated rows check

raw[raw.duplicated()]

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
19714	NaN	coimbres16del02	29	4.6	11.010375	76.95295	11.060375	77.00295	buffet	motorcycle	25

# Droping Duplicated rows

raw =  raw.drop_duplicates().reset_index(drop=True)

raw.shape

(45592, 11)

raw[raw.duplicated()]

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)

• Validating Each Column Data

                                        ID

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.ID.nunique())

print()

print("Unique Classes:")
raw.ID.unique()

Number of Unique Classes in Column: 36370

Unique Classes:

array([nan, 'b379', '5d6d', ..., '4f8d', '5eee', '5fb2'], dtype=object)

• Data is valid and belongs to ID names
• Long text in the names, making it short
• We have special characters in column data, need to handle them

raw.ID.unique()

array([nan, 'b379', '5d6d', ..., '4f8d', '5eee', '5fb2'], dtype=object)

                               Delivery_person_ID         

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.Delivery_person_ID.nunique())

print()

print("Unique Classes:")
raw.Delivery_person_ID.unique()

Number of Unique Classes in Column: 1320

Unique Classes:

array(['indores13del02', 'bangres18del02', 'bangres19del01', ...,
       'kocres04del01', 'bhpres06del03', 'goares03del03'], dtype=object)

• Column data is valid and belong to person id data
• Column is in object data type, need to convert it into datetime

raw.Delivery_person_ID.head()

0     indores13del02
1     bangres18del02
2     bangres19del01
3    coimbres13del02
4     chenres12del01
Name: Delivery_person_ID, dtype: object

• Feature Engineering Step

  • Taking Date of Journey , Deciding how to use it for modeling

raw.head(2)

                                         Delivery_person_Age

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.Delivery_person_Age.nunique())

print()

print("Unique Classes:")
raw.Delivery_person_Age.unique()

Number of Unique Classes in Column: 22

Unique Classes:

array([37, 34, 23, 38, 32, 22, 33, 35, 36, 21, 24, 29, 25, 31, 27, 26, 20,
       28, 39, 30, 15, 50], dtype=int64)

• Data is valid and belongs to column

                                    Delivery_person_Ratings

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.Delivery_person_Ratings.nunique())

print()

print("Unique Classes:")
raw.Delivery_person_Ratings.unique()

Number of Unique Classes in Column: 28

Unique Classes:

array([4.9, 4.5, 4.4, 4.7, 4.6, 4.8, 4.2, 4.3, 4. , 4.1, 5. , 3.5, 3.8,
       3.9, 3.7, 2.6, 2.5, 3.6, 3.1, 2.7, 1. , 3.2, 3.3, 6. , 3.4, 2.8,
       2.9, 3. ])

• Data is valid and belongs to column

                                     Restaurant_latitude

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.Restaurant_latitude.nunique())

print()

print("Unique Classes:")
raw.Restaurant_latitude.unique()

Number of Unique Classes in Column: 657

Unique Classes:

array([ 22.745049,  12.913041,  12.914264,  11.003669,  12.972793,
        17.431668,  23.369746,  12.352058,  17.433809,  30.327968,
        10.003064,  18.56245 ,  30.899584,  26.463504,  19.176269,
        12.311072,  18.592718,  17.426228,  22.552672,  18.563934,
        23.357804,  12.986047,  19.221315,  13.005801,  26.849596,
        21.160522,  12.934179,  18.51421 ,  11.022477,  21.160437,
        15.51315 ,  15.561295,   0.      ,  18.55144 ,  18.593481,
        21.173343,  17.451976,  12.972532,  13.064181,  19.121999,
        21.149569,  19.091458,  22.539129,  12.970324,  21.175975,
        11.003681,  10.96185 ,  27.165108,  26.88842 ,  26.913987,
        12.3085  ,  21.183434,  19.254567,  25.449659,  30.372202,
        21.157735,  21.186438,  12.935662,  17.431477,  12.933298,
        22.311358,  12.934365,  13.086438,  26.913483,  17.411028,
        18.516216,  15.5696  ,  19.876428,  12.939496,  23.374878,
       -27.163303,  22.74806 ,  26.891191,  12.316967,  18.927584,
        11.022298,  12.325461,  18.530963,  17.458998,  19.1813  ,
        22.727021,  12.979166,  18.994237,  12.284747,  11.000762,
        26.479108,  21.175104,  12.975377,  23.359194,  12.297954,
        23.359407,  23.416792,  26.892312,  22.31279 ,  22.761593,
        11.026117,  26.483042,  23.359033,  22.569358,  21.157729,
        22.311603,  18.536562,  22.745536,  18.543626,  17.41233 ,
        21.17106 ,  21.186884,  19.065838,  12.98041 ,  12.310972,
        11.025083,  22.538731,  11.001753,  22.310237,  22.725748,
        17.450851,  13.081878,  10.035573,  22.308096,  12.975996,
        19.178321,  26.910262,  25.443994,  12.321214,  19.055831,
        12.337928,  23.351489,  22.728163,  13.026286,  26.766536,
        21.149834,  21.149669,  26.482581,  26.911378,  13.02978 ,
        12.323194,  22.569367,  12.949934,  22.725835,  30.89286 ,
        12.933284,  17.429585,  12.978453,  13.044694,  26.846156,
        26.911927,  10.020683,  15.585658,  22.310526,  19.103249,
        26.471617,  19.866969,  12.906229,  22.751857,  11.010375,
        17.483216,  27.160934,  11.006686,  25.457687,  19.12663 ,
        19.875016,  18.536718,  13.026279,  17.455894,  13.054347,
        12.979096,  27.163303,  26.482419,  22.307898,  27.160832,
        10.027014,  30.890184,  21.15276 ,  22.753839,  19.876106,
        30.328174,  13.058616, -27.165108,  21.170096,  30.905562,
       -18.55144 ,  26.902328,  26.90519 ,  17.428294,  12.323978,
        26.492106,  19.003517,  10.994136,  23.353783,  17.440827,
        12.304569,  12.299524,  19.131141,  19.879631,  18.520016,
        21.170798,  22.760072,  22.761226,  23.354422,  22.722634,
        12.972161,  22.526461,  23.371292,  13.029198,  18.546258,
        26.921411,  11.008638,  13.049645,  17.430448,  19.874733,
        13.045479,  26.956431,  13.022394,  22.75004 ,  21.173493,
        13.091809,  18.533811,  15.496162,  26.913726,  30.895817,
       -30.366322,  12.323994,  19.120083,  12.337978,  27.161661,
        13.066762,  30.362686,  17.410371,   9.979186,  18.539299,
        22.310329,  11.022169,  19.875908,  10.006881,  12.326356,
        19.875522,  22.552996,  30.895204,  25.450329,  18.546947,
        30.340722,  22.651847,  25.45235 ,  17.424114,  12.323225,
       -12.325461,  26.905287,  17.422819,  17.45971 ,  22.695207,
        18.569156,  11.003008,  26.914142,  30.359722,  12.334022,
        23.214294, -12.316967,  12.970221,  22.753659,  18.636215,
        22.751234,  23.234249,  25.457116,  22.744648,  23.39925 ,
        19.874103,  22.577821,  22.514119,  22.311844,  18.994049,
        22.732225,  30.914057,  23.233219,  12.981615,  23.184992,
        26.49095 ,  22.551084,  11.021278,  30.899992,  11.024839,
        23.351058,  19.876219,  19.880256,  13.027018,  30.893234,
        30.319528,  27.195928, -23.359194,  25.450317,  21.185047,
        23.355164,  23.232537,  18.53408 ,  26.902908,  11.016298,
        10.000706,  25.454697,  21.186608,  26.90294 ,  23.214459,
        23.374989,  30.893244,  30.885915,  17.438263,  15.544419,
        26.474986,   9.970717,  30.873988,  19.888716,  22.547186,
        23.232357,  23.234631,  19.1093  ,  22.32    , -26.47775 ,
        26.469003,  26.483672,  10.027364, -15.546594,  19.207222,
        19.874449, -12.337978,  11.02091 ,  19.22384 ,  30.366322,
        30.361281,  26.473698,  18.554382,  22.553227, -27.160934,
        30.332735,   9.982834,  15.498603,  22.5491  ,  25.450377,
        26.471529, -26.90519 ,  23.218998,  23.235123,  22.533662,
        30.885814, -17.45971 ,  15.574828,  25.459775,  30.893081,
        22.514688, -26.914142,  23.266261,  23.333017, -19.176269,
        27.157772,  15.516833,  22.527893,  27.159795,  15.546594,
        15.157944,  27.16185 ,  30.88722 ,  27.158822, -23.230791,
        23.264015,  27.161694,   9.991703,  27.201725,   9.960846,
        26.481547, -22.539129,  15.303897,   9.988483,  25.454648,
        30.342509, -15.157944,  22.53796 ,  -9.959778,  30.346994,
       -15.51315 ,  15.49395 , -27.16185 ,  11.001852,  30.893384,
       -15.576683,  19.875337, -18.520016,  15.556561,  23.230791,
       -26.891191,  15.56155 ,  30.335259, -22.538999,  26.47775 ,
       -19.103249, -30.902872,   9.985497,  25.451517, -22.526461,
        26.472001,   9.959778,  -9.988483, -23.211529,  30.892978,
        15.506205,  19.878028, -30.895817, -26.474986,  22.538999,
         9.957144,  22.515082,  25.451646, -15.5696  , -12.311072,
       -26.913987, -12.970324,   9.979363,  10.028047, -21.157735,
       -23.374989, -13.049645,  26.474133,  30.902872,   9.985697,
       -26.913726,  23.211529,  19.876994, -22.310329, -23.234249,
        15.576683,  22.514585,  25.449872,  25.453436,   9.966783,
       -25.45235 , -17.451976, -21.186884, -26.472001, -26.910262,
       -11.003681, -11.003669, -13.054347, -30.372202, -12.972793,
       -25.449872, -18.569156, -18.546258, -17.429585, -27.159795,
       -22.553227, -22.761593,  -9.966783, -13.086438, -19.878028,
       -17.440827,  -9.970717, -12.913041, -12.970221, -19.876994,
       -19.875522, -25.449659, -30.346994, -22.552672, -22.311603,
       -12.352058, -25.450317, -21.15276 , -12.949934, -26.49095 ,
       -25.443994, -30.319528, -10.028047, -12.323978, -19.876106,
        26.47    , -18.53408 , -26.463504, -30.899584, -22.311844,
       -17.438263, -23.39925 , -23.234631, -30.361281, -19.091458,
       -17.483216, -15.496162, -22.725835, -30.885915, -22.53796 ,
       -12.972161, -23.184992, -17.424114, -10.035573, -19.22384 ,
       -23.235123, -25.453436, -23.359033, -19.221315, -23.214459,
       -21.173343, -26.483042, -21.183434, -22.310526, -22.74806 ,
       -12.304569, -19.1093  , -19.879631, -19.874449, -10.020683,
       -19.875337, -26.911927, -23.416792, -21.186608, -12.323994,
       -12.933284, -17.450851, -19.875016, -22.569358, -12.284747,
       -23.355164, -18.592718, -27.201725, -22.311358, -19.207222,
       -13.022394, -21.149569, -22.538731, -27.161661, -27.160832,
       -13.066762, -10.96185 , -12.935662, -17.428294, -30.359722,
       -11.022169, -30.885814, -25.451517, -22.651847, -22.547186,
       -26.474133, -30.893384, -22.761226, -11.001852, -30.327968,
       -22.745536, -17.455894, -26.481547, -22.32    , -23.371292,
       -18.563934, -30.905562, -25.450377, -22.515082, -22.725748,
       -23.351058, -26.473698, -23.333017, -30.873988,  -9.982834,
       -22.514119, -21.170798, -12.979166, -19.065838, -19.876428,
       -13.02978 , -12.310972, -22.307898, -21.170096, -21.186438,
        -9.960846, -10.994136, -21.175104, -15.303897, -11.022477,
       -26.846156, -25.457116, -26.905287, -21.149834, -30.328174,
       -30.362686, -19.055831, -22.5491  , -26.471529, -12.934365,
       -30.893244, -19.888716, -12.975377, -22.551084, -15.561295,
       -22.695207, -26.956431,  -9.979186, -11.006686, -12.323225,
       -13.026286, -25.454697, -23.357804, -19.880256, -13.081878,
       -10.027014, -18.543626, -10.003064, -22.552996, -18.530963,
       -19.866969, -22.75004 , -26.469003, -26.766536, -12.326356,
       -22.577821, -22.569367, -23.232357, -13.091809, -12.933298,
       -22.533662, -22.745049, -26.921411, -23.374878, -27.158822,
       -12.906229, -13.044694, -13.045479, -12.98041 , -26.90294 ,
       -23.369746, -19.875908, -26.479108, -30.892978, -23.232537,
       -22.514585, -22.751234, -17.426228, -17.411028, -18.636215,
       -15.56155 , -19.254567, -25.457687, -15.506205, -15.516833,
       -11.02091 , -12.3085  , -26.47    , -19.003517, -30.890184,
       -22.760072, -22.722634, -19.12663 , -22.753839, -21.173493,
       -17.430448, -12.939496, -18.56245 , -22.308096, -23.264015,
       -26.482581, -15.498603, -18.51421 , -12.975996, -19.874733,
       -15.49395 , -12.978453])

• Data is valid and belongs to column

                                  Restaurant_longitude

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.Restaurant_longitude.nunique())

print()

print("Unique Classes:")
raw.Restaurant_longitude.unique()

Number of Unique Classes in Column: 518

Unique Classes:

array([ 75.892471,  77.683237,  77.6784  ,  76.976494,  80.249982,
        78.408321,  85.33982 ,  76.60665 ,  78.386744,  78.046106,
        76.307589,  73.916619,  75.809346,  80.372929,  72.836721,
        76.654878,  73.773572,  78.407495,  88.352885,  73.915367,
        85.325146,  80.218114,  72.862381,  80.250744,  75.800512,
        72.771477,  77.615797,  73.838429,  76.995667,  72.774209,
        73.78346 ,  73.749478,   0.      ,  73.804855,  73.785901,
        72.792731,  78.385883,  77.608179,  80.236442,  72.908493,
        72.772697,  72.827808,  88.365507,  77.645748,  72.795503,
        76.975525,  76.971082,  78.015053,  75.800689,  75.752891,
        76.665808,  72.814492,  72.848923,  81.839744,  78.077151,
        72.768778,  72.794115,  77.61413 ,  78.40035 ,  77.614293,
        73.164798,  77.616155,  80.220672,  75.803139,  78.329645,
        73.842527,  73.742294,  75.364792,  77.625999,  85.335739,
        78.057044,  75.8934  ,  75.802083,  76.603067,  72.832585,
        76.998349,  76.632278,  73.828972,  78.500366,  72.836191,
        75.884167,  77.640709,  72.825553,  76.625861,  76.981876,
        80.315042,  72.804342,  77.696664,  85.325447,  76.665169,
        85.325055,  85.316842,  75.806896,  73.170283,  75.886362,
        76.944652,  80.317833,  85.325347,  88.433452,  72.768726,
        73.165012,  73.896485,  75.893106,  73.905101,  78.449654,
        72.789292,  72.793616,  72.832658,  77.640489,  76.659264,
        77.015393,  88.364878,  76.986241,  73.158921,  75.898497,
        78.379347,  80.248519,  76.336958,  73.167753,  80.221898,
        72.834715,  75.783013,  81.860187,  76.621094,  72.833984,
        76.617889,  85.324253,  75.884212,  80.275235,  75.837333,
        72.778666,  72.772629,  80.315628,  75.789034,  80.208812,
        76.630583,  88.433187,  77.699386,  75.887648,  75.822199,
        77.615428,  78.392621,  77.643685,  80.26147 ,  75.8023  ,
        75.797282,  76.310631,  73.743606,  73.170937,  72.846749,
        80.313564,  75.318894,  77.596791,  75.866699,  76.95295 ,
        78.552111,  78.044095,  76.951736,  81.835585,  72.829976,
        75.322405,  73.830327,  80.174568,  78.375467,  80.257221,
        77.640625,  80.320939,  73.167788,  78.011608,  76.308053,
        75.829615,  72.778059,  75.897429,  75.340775,  78.049117,
        80.264151,  72.789122,  75.832841, -73.804855,  75.794257,
        75.810753,  78.404423,  76.627961,  80.327797,  72.82765 ,
        76.963303,  85.326967,  78.393391,  76.643622,  76.64262 ,
        72.813074,  75.323403,  73.830547,  72.790489,  75.892574,
        75.887522,  85.3329  ,  75.886959,  77.596014,  88.364453,
        85.327872,  77.570997,  73.904337,  75.793604,  76.984311,
        80.242268,  78.418213,  75.353942,  80.23311 ,  75.776649,
        80.242439,  75.902847,  72.801953,  80.219104,  73.899315,
        73.825364,  75.75282 ,  75.813112, -78.070453,  76.626167,
        72.907385,  76.616792,  78.011544,  80.251865,  78.06889 ,
        78.437225,  76.317361,  73.897902,  73.169083,  76.999594,
        75.358888,  76.345397,  76.619103,  75.367127,  88.35231 ,
        75.822103,  81.834279,  73.900626,  78.060221,  75.881991,
        81.841889,  78.347554,  76.630028, -76.632278,  75.794592,
        78.449578,  78.368855,  75.866059,  73.774723,  76.97544 ,
        75.805704,  78.067079,  76.618203,  77.435361,  77.645396,
        75.903365,  73.751081,  75.88949 ,  77.434007,  81.859682,
        75.894377,  85.390464,  75.368419,  88.400581,  88.362504,
        73.165081,  72.825203,  75.874765,  75.83982 ,  77.433571,
        80.231598,  77.417227,  80.318656,  88.354127,  76.995017,
        75.831338,  77.007003,  85.325731,  75.346017,  75.323503,
        80.254791,  75.82172 ,  78.040267,  77.998092, -85.325447,
        81.831681,  72.80859 ,  85.324097,  77.429845,  73.89852 ,
        75.792934,  76.972076,  76.349516,  81.834492,  72.794136,
        75.793007,  77.434976,  85.335486,  75.821817,  75.788259,
        78.397865,  73.755736,  80.342796,  76.285447,  75.842739,
        75.321461,  88.35068 ,  77.429989,  77.401663,  72.825451,
        73.17    , -80.351569,  80.316344,  80.320708,  76.308258,
        73.760431,  72.972281,  75.360232,  76.940432,  72.841347,
        78.070453,  78.068022,  80.352677,  73.798206,  88.353273,
       -78.044095,  78.054222,  76.283268,  73.826911,  88.400467,
        81.834236,  80.313458, -75.810753,  77.373573,  77.398886,
        88.366217,  75.786976,  73.766883,  81.834841,  75.821495,
        88.393294,  77.379605,  85.3172  , -72.836721,  78.04725 ,
        73.768172,  88.368628,  78.04299 ,  73.950889,  78.040165,
        75.804893,  78.045359,  77.43702 ,  77.408236,  78.034714,
        76.293136,  78.007553,  76.293936,  80.299775,  73.914336,
        76.295211,  81.834502,  78.061187,  88.349843,  76.296106,
        78.062543,  73.827423, -78.040165,  76.976268,  75.821202,
        73.75575 ,  75.316722, -73.830547,  73.763633,  73.749092,
        78.053162, -88.322337,  80.351569, -72.846749,  75.826808,
        76.276999,  81.832616,  80.354002,  77.419399,  75.821847,
        73.766668,  75.317475,  88.322337,  76.296783,  88.36783 ,
        81.832796, -76.654878, -77.645748,  76.285001,  76.310019,
       -80.242268,  80.3481  ,  76.281128,  75.372353, -73.169083,
       -77.434007,  88.39331 ,  81.836167,  81.833167,  76.242981,
       -81.841889, -78.385883, -75.783013, -76.975525, -76.976494,
       -80.257221, -78.077151, -73.904337, -78.392621, -78.04299 ,
       -75.367127, -78.062543, -88.352885, -76.60665 , -77.699386,
       -81.860187,  80.35    , -73.165081, -78.397865, -72.827808,
       -75.887648, -85.335486, -77.596014, -78.347554, -72.841347,
       -72.862381, -72.792731, -72.814492, -76.643622, -85.316842,
       -76.626167, -77.615428, -78.379347, -76.625861, -73.164798,
       -72.972281, -77.683237, -78.011544, -80.251865, -77.61413 ,
       -88.349843, -76.999594, -75.881991, -75.821202, -75.887522,
       -76.976268, -78.375467, -73.17    , -75.832841, -81.834236,
       -85.325731, -85.3172  , -76.283268, -77.640709, -78.068022,
       -80.208812, -72.789122, -75.340775, -72.804342, -76.995667,
       -81.859682, -75.794592, -78.06889 , -76.963303, -77.616155,
       -77.696664, -75.866059, -76.951736, -76.630028, -85.325146,
       -73.828972, -75.902847, -80.316344, -76.619103, -80.242439,
       -76.293936, -77.614293, -88.366217, -75.837333, -75.892471,
       -75.793604, -80.248519, -78.045359, -77.596791, -80.26147 ,
       -80.23311 , -77.640489, -80.220672, -75.793007, -85.33982 ,
       -78.407495, -73.773572, -76.603067, -73.751081, -76.971082,
       -75.75282 , -78.404423, -76.940432, -76.665808, -73.742294,
       -77.43702 , -75.829615, -72.772697, -75.897429, -80.318656,
       -72.801953, -72.832658, -73.916619, -77.408236, -73.838429,
       -72.778059, -73.827423, -77.643685])

• Data is valid and belongs to column

                                   Delivery_location_latitude

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw. Delivery_location_latitude.nunique())

print()

print("Unique Classes:")
raw. Delivery_location_latitude.unique()

Number of Unique Classes in Column: 4373

Unique Classes:

array([22.765049, 13.043041, 12.924264, ..., 30.982872, 15.636205,
       20.005337])

• Data is valid and belongs to column

                                 Delivery_location_longitude

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.Delivery_location_longitude.nunique())

print()

print("Unique Classes:")
raw.Delivery_location_longitude.unique()

Number of Unique Classes in Column: 4373

Unique Classes:

array([75.912471, 77.813237, 77.6884  , ..., 75.906808, 73.896668,
       75.446722])

• Data is valid and belongs to column

                                             Type_of_order  

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.Type_of_order.nunique())

print()

print("Unique Classes:")
raw.Type_of_order.unique()

Number of Unique Classes in Column: 4

Unique Classes:

array(['snack ', 'drinks ', 'buffet ', 'meal '], dtype=object)

• Data is valid and belongs to column

                                          Type_of_vehicle

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw.Type_of_vehicle.nunique())

print()

print("Unique Classes:")
raw.Type_of_vehicle.unique()

Number of Unique Classes in Column: 4

Unique Classes:

array(['motorcycle ', 'scooter ', 'electric_scooter ', 'bicycle '],
      dtype=object)

• Data is valid and belongs to column

                                          Time_taken(min)

# Checking number of unique values & unique values in col data

print("Number of Unique Classes in Column:", raw['Time_taken(min)'].nunique())

print()

print("Unique Classes:")
raw['Time_taken(min)'].unique()

Number of Unique Classes in Column: 45

Unique Classes:

array([24, 33, 26, 21, 30, 40, 32, 34, 46, 23, 20, 41, 15, 36, 39, 18, 38,
       47, 12, 22, 25, 35, 10, 19, 11, 28, 52, 16, 27, 49, 17, 14, 37, 44,
       42, 31, 13, 29, 50, 43, 48, 54, 53, 45, 51], dtype=int64)

• Data is valid and belongs to column

Final Validated Data

raw.head(2)

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	NaN	indores13del02	37	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle	24
1	b379	bangres18del02	34	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter	33

2. EDA (Data Understanding)

Back to Top

• In EDA we can do Data Analysis in two methods
  • Uni-Variate Analysis (Study of Individual column Data)
    • Descriptive + Visual Analysis
  • Bi-Varaite Analysis (Study data between two columns)
    • Descriptive + Visual Analysis
  • Multi-Variate Analysis (Study data between three or more columns)
    • Descriptive Stats (For Regression project , corr matrix)

# Taking copy of raw data

data = raw.copy()

data.head(2)

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	NaN	indores13del02	37	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle	24
1	b379	bangres18del02	34	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter	33

2.1 Uni-Variate Analysis

pip install simple_colors

Requirement already satisfied: simple_colors in c:\users\vinit\anaconda3\lib\site-packages (0.1.5)
Note: you may need to restart the kernel to use updated packages.

# Viz Libraries

import matplotlib.pyplot as plt
import seaborn as sns

import plotly.express as px

import warnings
warnings.filterwarnings("ignore")

• Taking user-defined module eda

from eda import uni_variate_analysis

uni_variate_analysis(raw)

Column Name: ID
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Classes: [nan 'b379' '5d6d' ... '4f8d' '5eee' '5fb2']

Number of Classes: 36370

Value Counts of each class:
ID
bbd 8.00    2
afa 2.00    2
bef 1.00    2
cdf 5.00    2
bec 8.00    2
            ..
44d7         1
5ad3         1
358b         1
621d         1
5fb2         1
Name: count, Length: 36370, dtype: int64

Each Class percentage:
ID
bbd 8.00    0.0
afa 2.00    0.0
bef 1.00    0.0
cdf 5.00    0.0
bec 8.00    0.0
            ... 
44d7         0.0
5ad3         0.0
358b         0.0
621d         0.0
5fb2         0.0
Name: count, Length: 36370, dtype: float64

Mode value: aed 1.00

*****************************************
      Visual Analysis - Pie Charts      
*****************************************

?????? Too many classes to Show ????????????????


Column Name: Delivery_person_ID
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Classes: ['indores13del02' 'bangres18del02' 'bangres19del01' ... 'kocres04del01'
 'bhpres06del03' 'goares03del03']

Number of Classes: 1320

Value Counts of each class:
Delivery_person_ID
puneres01del01    67
japres11del02     67
hydres04del02     66
japres03del01     66
vadres11del02     66
                  ..
dehres18del03      7
aurgres11del03     7
kolres09del03      6
kocres16del03      6
bhpres010del03     5
Name: count, Length: 1320, dtype: int64

Each Class percentage:
Delivery_person_ID
puneres01del01    0.15
japres11del02     0.15
hydres04del02     0.14
japres03del01     0.14
vadres11del02     0.14
                  ... 
dehres18del03     0.02
aurgres11del03    0.02
kolres09del03     0.01
kocres16del03     0.01
bhpres010del03    0.01
Name: count, Length: 1320, dtype: float64

Mode value: japres11del02

*****************************************
      Visual Analysis - Pie Charts      
*****************************************

?????? Too many classes to Show ????????????????


Column Name: Delivery_person_Age
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Mean: 29.54
Median: 29.0
Mode of the Delivery_person_Age Col: 29

Min & Max: (15, 50)
Standard Deviation: 5.7

Skewness: 0.03
Kurtosis: -0.98

********************************************************
 Visual Analysis - Distplot (Histogram + Desnsity plot) 
********************************************************

Column Name: Delivery_person_Ratings
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Mean: 4.63
Median: 4.7
Mode of the Delivery_person_Ratings Col: 4.6

Min & Max: (1.0, 6.0)
Standard Deviation: 0.33

Skewness: -2.53
Kurtosis: 16.43

********************************************************
 Visual Analysis - Distplot (Histogram + Desnsity plot) 
********************************************************

Column Name: Restaurant_latitude
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Mean: 17.02
Median: 18.5491935
Mode of the Restaurant_latitude Col: 0.0

Min & Max: (-30.905562, 30.914057)
Standard Deviation: 8.19

Skewness: -1.36
Kurtosis: 3.71

********************************************************
 Visual Analysis - Distplot (Histogram + Desnsity plot) 
********************************************************

Column Name: Restaurant_longitude
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Mean: 70.23
Median: 75.898497
Mode of the Restaurant_longitude Col: 0.0

Min & Max: (-88.366217, 88.433452)
Standard Deviation: 22.88

Skewness: -3.22
Kurtosis: 10.3

********************************************************
 Visual Analysis - Distplot (Histogram + Desnsity plot) 
********************************************************

Column Name: Delivery_location_latitude
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Mean: 17.47
Median: 18.633934
Mode of the Delivery_location_latitude Col: 0.13

Min & Max: (0.01, 31.054057)
Standard Deviation: 7.34

Skewness: -0.7
Kurtosis: 0.26

********************************************************
 Visual Analysis - Distplot (Histogram + Desnsity plot) 
********************************************************

Column Name: Delivery_location_longitude
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Mean: 70.85
Median: 76.002574
Mode of the Delivery_location_longitude Col: 0.13

Min & Max: (0.01, 88.563452)
Standard Deviation: 21.12

Skewness: -2.96
Kurtosis: 7.1

********************************************************
 Visual Analysis - Distplot (Histogram + Desnsity plot) 
********************************************************

Column Name: Type_of_order
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Classes: ['snack ' 'drinks ' 'buffet ' 'meal ']

Number of Classes: 4

Value Counts of each class:
Type_of_order
snack      11533
meal       11458
drinks     11322
buffet     11279
Name: count, dtype: int64

Each Class percentage:
Type_of_order
snack      25.30
meal       25.13
drinks     24.83
buffet     24.74
Name: count, dtype: float64

Mode value: snack 

*****************************************
      Visual Analysis - Pie Charts      
*****************************************

Column Name: Type_of_vehicle
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Classes: ['motorcycle ' 'scooter ' 'electric_scooter ' 'bicycle ']

Number of Classes: 4

Value Counts of each class:
Type_of_vehicle
motorcycle           26434
scooter              15276
electric_scooter      3814
bicycle                 68
Name: count, dtype: int64

Each Class percentage:
Type_of_vehicle
motorcycle           57.98
scooter              33.51
electric_scooter      8.37
bicycle               0.15
Name: count, dtype: float64

Mode value: motorcycle 

*****************************************
      Visual Analysis - Pie Charts      
*****************************************

Column Name: Time_taken(min)
============================================================

*****************************************
          Descriptive Stats              
*****************************************

Mean: 26.29
Median: 26.0
Mode of the Time_taken(min) Col: 26

Min & Max: (10, 54)
Standard Deviation: 9.38

Skewness: 0.49
Kurtosis: -0.31

********************************************************
 Visual Analysis - Distplot (Histogram + Desnsity plot) 
********************************************************

Insights:

• Delivery person ID
  • 15% of orders were dilivery by puneres01dl01& japres11del02
  • lowest count of orders were delivery by person having ID of kolres09del03,kocres16del03,bhpres010del03 with 0.01%
• Delivery_person_Age
  • Most of the delivery person age is under 29
  • Minimum age of the delivery person is 15 and maximum is 50
  • 75%of the delivery persons are under age 34
• Time taken:
  • Min - 10min , Max - 54 mis , Average - 26 min
  • 75% of the data shows that the time taken is 32 min
  • 25 % of the data shows the time taken is 19 min
• Ratings:
  • data shows that the delivery persons have minimum rating 1 and maximum rating 6and the average ratings are 4
• Restaurant_latitude
• Restaurant_longitude
• Delivery_location_latitude
• Delivery_location_longitude
• Type of order:
  • the food order dimealstributes a well balanced perference will 25.3%for snacks,25.13%for meal,24.83% for drinks24.74% for buffet
• Type of vehicle:
  • Most of the delivery partners used motor cycle(58%)

Further Business Queries:

2.2 Bi-Variate Analysis

data.head(2)

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	NaN	indores13del02	37	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle	24
1	b379	bangres18del02	34	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter	33

• Combinations of Imp Columns for Bi-Variate analysis
  • Consider columns that can show impact on while estimation of fare

• Time taken (numeric discrete) vs Delivery person age (numeric continous)

data[['Delivery_person_Age','Time_taken(min)']].corr()

	Delivery_person_Age	Time_taken(min)
Delivery_person_Age	1.000000	0.292708
Time_taken(min)	0.292708	1.000000

px.scatter(data, x='Delivery_person_Age', y="Time_taken(min)", width=650, height=350)

• There is a weak correlation between delivery person age and time taken.
• Age of delivery person increases, time taken to deliver an order tends to increase by 30%

• Restaurant latitude(numeric continuous) vs delivery location latitude(numeric continous)

# Descriptive Stats - Correlations

data[['Restaurant_latitude','Delivery_location_latitude']].corr()

	Restaurant_latitude	Delivery_location_latitude
Restaurant_latitude	1.000000	0.866376
Delivery_location_latitude	0.866376	1.000000

# Visual Analysis

px.scatter(data, x='Restaurant_latitude', y="Delivery_location_latitude", width=650, height=350)

• there is a strong positive correlation between Restaurant latitude and Delivery location latitude
• indicates that the latitude of the restaurant and the delivery location are closely related

data.corr(numeric_only=True)

	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Time_taken(min)
Delivery_person_Age	1.000000	-0.067449	-0.001956	-0.006795	0.002358	-0.000592	0.292708
Delivery_person_Ratings	-0.067449	1.000000	-0.004848	-0.011146	-0.010200	-0.013349	-0.331103
Restaurant_latitude	-0.001956	-0.004848	1.000000	0.661794	0.866376	0.602722	0.013978
Restaurant_longitude	-0.006795	-0.011146	0.661794	1.000000	0.632305	0.915026	0.007822
Delivery_location_latitude	0.002358	-0.010200	0.866376	0.632305	1.000000	0.690527	0.014240
Delivery_location_longitude	-0.000592	-0.013349	0.602722	0.915026	0.690527	1.000000	0.007626
Time_taken(min)	0.292708	-0.331103	0.013978	0.007822	0.014240	0.007626	1.000000

data.head(2)

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	NaN	indores13del02	37	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle	24
1	b379	bangres18del02	34	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter	33

data.groupby('Type_of_order')['Time_taken(min)'].mean()

Type_of_order
buffet     26.283624
drinks     26.187953
meal       26.419270
snack      26.286309
Name: Time_taken(min), dtype: float64

px.box(data, y='Type_of_order', x='Time_taken(min)')

• The average delivery times vary slightly across different types of orders

data.head(2)

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	NaN	indores13del02	37	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle	24
1	b379	bangres18del02	34	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter	33

(data.groupby('Type_of_vehicle')['Time_taken(min)'].mean()).sort_values(ascending=False)

Type_of_vehicle
motorcycle           27.605773
bicycle              26.426471
scooter              24.480754
electric_scooter     24.470110
Name: Time_taken(min), dtype: float64

px.box(data, y='Type_of_vehicle', x='Time_taken(min)')

• It takes for 27 min orders delivered by bicycle,24min for electric scootor and scootor,28min for motorcycle

pd.crosstab(data.Type_of_vehicle, data.Type_of_order, margins=True)

Type_of_order	buffet	drinks	meal	snack	All
Type_of_vehicle
bicycle	15	16	23	14	68
electric_scooter	943	949	948	974	3814
motorcycle	6594	6532	6657	6651	26434
scooter	3727	3825	3830	3894	15276
All	11279	11322	11458	11533	45592

• Most of the meal,snack orders are delivered by delivery partners having motorcycle

3. Missing Values & Outlier Handling

Back to Top

3.1 Missing Values

# Checking missing values count for each column

data.isnull().sum()

ID                             9184
Delivery_person_ID                0
Delivery_person_Age               0
Delivery_person_Ratings           0
Restaurant_latitude               0
Restaurant_longitude              0
Delivery_location_latitude        0
Delivery_location_longitude       0
Type_of_order                     0
Type_of_vehicle                   0
Time_taken(min)                   0
dtype: int64

• There is no missing or null values present in any of the columns within the dataset

• 3.2 Outliers (Numeric Columns)
• Extreme data points in data, either lower or higher

numdata = data.select_dtypes(exclude="O")

numdata.head(2)

	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Time_taken(min)
0	37	4.9	22.745049	75.892471	22.765049	75.912471	24
1	34	4.5	12.913041	77.683237	13.043041	77.813237	33

from Outlier import outlier_detect

outlier_detect(data)

Column: Delivery_person_Age
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50]

Column: Delivery_person_Ratings
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

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Upper Outliers:

[6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0]

Column: Restaurant_latitude
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

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Upper Outliers:

[]

Column: Restaurant_longitude
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

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Column: Delivery_location_latitude
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[]

Column: Delivery_location_longitude
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

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Upper Outliers:

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88.463452, 88.464127, 88.464127, 88.464127, 88.464127, 88.470467, 88.470467, 88.470581, 88.470581, 88.470581, 88.472504, 88.472504, 88.473187, 88.473187, 88.473187, 88.473294, 88.473294, 88.473294, 88.473294, 88.47331, 88.47331, 88.47331, 88.47331, 88.473452, 88.473452, 88.474453, 88.474453, 88.474453, 88.474453, 88.474878, 88.474878, 88.474878, 88.475507, 88.475507, 88.475507, 88.475507, 88.476217, 88.476217, 88.47783, 88.47783, 88.478628, 88.478628, 88.478628, 88.478628, 88.479843, 88.479843, 88.480467, 88.480467, 88.480467, 88.480467, 88.480581, 88.480581, 88.480581, 88.48068, 88.48068, 88.48068, 88.48231, 88.48231, 88.48231, 88.482885, 88.482885, 88.482885, 88.483187, 88.483187, 88.483187, 88.483273, 88.483273, 88.483273, 88.483294, 88.483294, 88.483294, 88.48331, 88.48331, 88.48331, 88.48331, 88.483452, 88.483452, 88.484127, 88.484127, 88.490467, 88.490467, 88.490467, 88.490467, 88.490581, 88.490581, 88.490581, 88.492504, 88.492504, 88.492504, 88.493187, 88.493187, 88.493187, 88.493187, 88.493452, 88.493452, 88.493452, 88.493452, 88.494453, 88.494878, 88.494878, 88.494878, 88.494878, 88.495507, 88.495507, 88.495507, 88.495507, 88.496217, 88.496217, 88.496217, 88.496217, 88.49783, 88.49783, 88.49783, 88.49783, 88.498628, 88.498628, 88.503187, 88.503187, 88.503187, 88.503187, 88.503294, 88.503294, 88.503294, 88.50331, 88.50331, 88.50331, 88.50331, 88.503452, 88.503452, 88.503452, 88.503452, 88.510467, 88.510467, 88.510581, 88.510581, 88.510581, 88.510581, 88.513187, 88.513187, 88.513187, 88.513452, 88.513452, 88.513452, 88.523187, 88.523187, 88.523294, 88.523294, 88.52331, 88.52331, 88.52331, 88.52331, 88.523452, 88.523452, 88.523452, 88.523452, 88.530467, 88.530467, 88.530467, 88.530581, 88.530581, 88.530581, 88.530581, 88.543187, 88.543187, 88.543187, 88.543187, 88.543452, 88.543452, 88.543452, 88.563187, 88.563187, 88.563187, 88.563187, 88.563452, 88.563452]

Column: Time_taken(min)
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54]

from Outlier import outlier_replacement

outlier_replacement(data)

Column: Delivery_person_Age
------------------------------------------------
Replacing Outliers with 5th percentile for lower Outliers, 95th percentile for Upper Outliers....
Adjust the module code for any other replacements.........

Column: Delivery_person_Ratings
------------------------------------------------
Replacing Outliers with 5th percentile for lower Outliers, 95th percentile for Upper Outliers....
Adjust the module code for any other replacements.........

Column: Restaurant_latitude
------------------------------------------------
Replacing Outliers with 5th percentile for lower Outliers, 95th percentile for Upper Outliers....
Adjust the module code for any other replacements.........

Column: Restaurant_longitude
------------------------------------------------
Replacing Outliers with 5th percentile for lower Outliers, 95th percentile for Upper Outliers....
Adjust the module code for any other replacements.........

Column: Delivery_location_latitude
------------------------------------------------
Replacing Outliers with 5th percentile for lower Outliers, 95th percentile for Upper Outliers....
Adjust the module code for any other replacements.........

Column: Delivery_location_longitude
------------------------------------------------
Replacing Outliers with 5th percentile for lower Outliers, 95th percentile for Upper Outliers....
Adjust the module code for any other replacements.........

Column: Time_taken(min)
------------------------------------------------
Replacing Outliers with 5th percentile for lower Outliers, 95th percentile for Upper Outliers....
Adjust the module code for any other replacements.........

outlier_detect(data)

Column: Delivery_person_Age
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[]

Column: Delivery_person_Ratings
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[]

Column: Restaurant_latitude
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[]

Column: Restaurant_longitude
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[]

Column: Delivery_location_latitude
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[]

Column: Delivery_location_longitude
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[]

Column: Time_taken(min)
------------------------------------------------
Boxplot For Outlier Identification:

Lower Outliers:

[]

Upper Outliers:

[]

4. Predictive Modeling

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• Building a predictive model/trained algorithm to get the relation betweeen one col(y) to other columns (X)

  y ~ f(x)+e

4.1 Selecting X & y

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• Selecting Output column (y) - future prediction column & Input column/columns (X) - Reference columns

  • X (independent variables/input columns/explanatory variables)
  • y (dependent variable/output column/response column

We have

data.head()

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	Time_taken(min)
0	NaN	indores13del02	37.0	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle	24.0
1	b379	bangres18del02	34.0	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter	33.0
2	5d6d	bangres19del01	23.0	4.4	12.914264	77.678400	12.924264	77.688400	drinks	motorcycle	26.0
3	7a6a	coimbres13del02	38.0	4.7	11.003669	76.976494	11.053669	77.026494	buffet	motorcycle	21.0
4	70a2	chenres12del01	32.0	4.6	12.972793	80.249982	13.012793	80.289982	snack	scooter	30.0

• For this we dataset we want to estimate Food Delivery , taking Time_taken(min) column as Output (y)
  • Remaining Columns data can be taken as input (X)

# Input & Output

X = data.drop('Time_taken(min)', axis=1)
y = data['Time_taken(min)']

print("Output Column Data (y):")
display(y.head())
print()
print("Input Columns Data (X):")
display(X.head())

Output Column Data (y):

0    24.0
1    33.0
2    26.0
3    21.0
4    30.0
Name: Time_taken(min), dtype: float64

Input Columns Data (X):

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle
0	NaN	indores13del02	37.0	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle
1	b379	bangres18del02	34.0	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter
2	5d6d	bangres19del01	23.0	4.4	12.914264	77.678400	12.924264	77.688400	drinks	motorcycle
3	7a6a	coimbres13del02	38.0	4.7	11.003669	76.976494	11.053669	77.026494	buffet	motorcycle
4	70a2	chenres12del01	32.0	4.6	12.972793	80.249982	13.012793	80.289982	snack	scooter

4.2 Feature Engineering of X

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• Generation/ Modification / Deletion / Selection of X columns/features according to y column

X.head()

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle
0	NaN	indores13del02	37.0	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle
1	b379	bangres18del02	34.0	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter
2	5d6d	bangres19del01	23.0	4.4	12.914264	77.678400	12.924264	77.688400	drinks	motorcycle
3	7a6a	coimbres13del02	38.0	4.7	11.003669	76.976494	11.053669	77.026494	buffet	motorcycle
4	70a2	chenres12del01	32.0	4.6	12.972793	80.249982	13.012793	80.289982	snack	scooter

• 4.2.1 Feature Generation

• Calculating distance by using restaurant latitude,longitude and delivery location latitude ,longitude

X.head(2)

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle
0	NaN	indores13del02	37.0	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle
1	b379	bangres18del02	34.0	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter

• 4.2.2 Feature Selection/Deletion

• ID and Delivery person ID columns will not be used for futher analysis,so we can drop them
• Also, we can drop Restaurant latitude,Restaurant longitude,Delivery location latitude,Delivery location longitude

pip install geopy

Requirement already satisfied: geopy in c:\users\vinit\anaconda3\lib\site-packages (2.4.1)
Requirement already satisfied: geographiclib<3,>=1.52 in c:\users\vinit\anaconda3\lib\site-packages (from geopy) (2.0)
Note: you may need to restart the kernel to use updated packages.

from geopy.distance import geodesic

for index, row in data.iterrows():
    origin = (row['Restaurant_latitude'],row['Restaurant_longitude'])
    destination = (row['Delivery_location_latitude'],row['Delivery_location_longitude'])
    
    #calculate the distance
    distance =round(geodesic(origin,destination).kilometers,2)
    X.at[index, 'distance'] = distance

X.head()

	ID	Delivery_person_ID	Delivery_person_Age	Delivery_person_Ratings	Restaurant_latitude	Restaurant_longitude	Delivery_location_latitude	Delivery_location_longitude	Type_of_order	Type_of_vehicle	distance
0	NaN	indores13del02	37.0	4.9	22.745049	75.892471	22.765049	75.912471	snack	motorcycle	3.02
1	b379	bangres18del02	34.0	4.5	12.913041	77.683237	13.043041	77.813237	snack	scooter	20.14
2	5d6d	bangres19del01	23.0	4.4	12.914264	77.678400	12.924264	77.688400	drinks	motorcycle	1.55
3	7a6a	coimbres13del02	38.0	4.7	11.003669	76.976494	11.053669	77.026494	buffet	motorcycle	7.77
4	70a2	chenres12del01	32.0	4.6	12.972793	80.249982	13.012793	80.289982	snack	scooter	6.20

columns_to_drop =['ID','Delivery_person_ID','Restaurant_latitude','Restaurant_longitude','Delivery_location_latitude','Delivery_location_longitude']
X.drop(columns_to_drop, axis=1, inplace=True, errors='ignore')

X.head()

	Delivery_person_Age	Delivery_person_Ratings	Type_of_order	Type_of_vehicle	distance
0	37.0	4.9	snack	motorcycle	3.02
1	34.0	4.5	snack	scooter	20.14
2	23.0	4.4	drinks	motorcycle	1.55
3	38.0	4.7	buffet	motorcycle	7.77
4	32.0	4.6	snack	scooter	6.20

4.2.3 Feature Modification (Data Pre-Processing)

Encoding

• Converting categorical data to numeric data

X.select_dtypes("O").head()

	Type_of_order	Type_of_vehicle
0	snack	motorcycle
1	snack	scooter
2	drinks	motorcycle
3	buffet	motorcycle
4	snack	scooter

X.head()

	Delivery_person_Age	Delivery_person_Ratings	Type_of_order	Type_of_vehicle	distance
0	37.0	4.9	snack	motorcycle	3.02
1	34.0	4.5	snack	scooter	20.14
2	23.0	4.4	drinks	motorcycle	1.55
3	38.0	4.7	buffet	motorcycle	7.77
4	32.0	4.6	snack	scooter	6.20

• For this data we have type of order,type of vehicle columns both are nominal columns so we can do onehotencoding

Saving above X Data as ref to UserInput Data For Real Time Prediction

X.to_csv("inpdata.csv", index=False)

Sklearn onehotencoding

# Sklearn function

from sklearn.preprocessing import OneHotEncoder

# Defining ohe object

ohe = OneHotEncoder(sparse=True, handle_unknown='ignore')

oheplace = ohe.fit_transform(X.select_dtypes("O")).toarray()

oheplace = pd.DataFrame(oheplace,columns=ohe.get_feature_names_out())

X = pd.concat([X, oheplace], axis=1)

X

	Delivery_person_Age	Delivery_person_Ratings	Type_of_order	Type_of_vehicle	distance	Type_of_order_buffet	Type_of_order_drinks	Type_of_order_meal	Type_of_order_snack	Type_of_vehicle_bicycle	Type_of_vehicle_electric_scooter	Type_of_vehicle_motorcycle	Type_of_vehicle_scooter
0	37.0	4.9	snack	motorcycle	3.02	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0
1	34.0	4.5	snack	scooter	20.14	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0
2	23.0	4.4	drinks	motorcycle	1.55	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
3	38.0	4.7	buffet	motorcycle	7.77	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
4	32.0	4.6	snack	scooter	6.20	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...
45587	30.0	4.8	meal	motorcycle	1.49	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0
45588	21.0	4.6	buffet	motorcycle	21.54	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
45589	30.0	4.9	drinks	scooter	4.65	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0
45590	20.0	4.7	snack	motorcycle	6.22	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0
45591	23.0	4.9	snack	scooter	9.14	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0

45592 rows × 13 columns

del X['Type_of_order']

del X['Type_of_vehicle']

X.head(2)

	Delivery_person_Age	Delivery_person_Ratings	distance	Type_of_order_buffet	Type_of_order_drinks	Type_of_order_meal	Type_of_order_snack	Type_of_vehicle_bicycle	Type_of_vehicle_electric_scooter	Type_of_vehicle_motorcycle	Type_of_vehicle_scooter
0	37.0	4.9	3.02	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0
1	34.0	4.5	20.14	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0

4.3 Train-Test Split of X & y

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• Dividing data into two parts, train-test
  • train part used for model building
  • test part is used for model evaluation

# sklearn method

from sklearn.model_selection import train_test_split

xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.30, random_state=123)

xtrain.shape,ytrain.shape

((31914, 11), (31914,))

xtest.shape,ytest.shape

((13678, 11), (13678,))

4.4 Modeling/Algorithm Training on Train Data

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• Sending xtrain & ytrain data to a algorithm, where it can study the patterns and gives predictive model to generate y for future x values

• Taken y data is numerical, we can apply machine learning supervised regression algorithms

• In regression we have below algorithms

  • Linear Algorithms (x data is linear to y)
    • Linear Regression (MLR)
    • Polynomial Regression
    • Regularization Models (if Linear Regression becomes overfit)
      • Lasso & Ridge
  • Non-Linear Algorithms (x data is not linear to y) - ML Supervised Classification Algorithms Used for regression
    • Knn Regressor
    • Svm Regressor
    • Decision Tree Regressor
    • RandomForest Regressor
    • Xgboost Regressor

Giving xtrain , ytrain data to all the above models to get better performance model

• Importing Sklearn Library Models Functions

pip install xgboost

Requirement already satisfied: xgboost in c:\users\vinit\anaconda3\lib\site-packages (2.0.3)
Requirement already satisfied: numpy in c:\users\vinit\anaconda3\lib\site-packages (from xgboost) (1.24.3)
Requirement already satisfied: scipy in c:\users\vinit\anaconda3\lib\site-packages (from xgboost) (1.11.1)
Note: you may need to restart the kernel to use updated packages.

from sklearn.linear_model import LinearRegression # MLR
from sklearn.preprocessing import PolynomialFeatures # Poly
from sklearn.linear_model import Lasso, Ridge # Lasso & Ridge
from sklearn.neighbors import KNeighborsRegressor # KNN
from sklearn.svm import SVR # SVM
from sklearn.tree import DecisionTreeRegressor # DT
from sklearn.ensemble import RandomForestRegressor # RF
from xgboost import XGBRegressor # XGB

• Model Objects Defining

# Multiple Linear Regression 

mlr = LinearRegression() # inside params - intercept (optional)

# Polynomial Regression

polyfeat = PolynomialFeatures(degree = 2)  # degree is hyperparam

poly = LinearRegression()

# Lasso (L1) & Ridge (L2)

lasso = Lasso(alpha = 5) # alpha - hyperparam - penalty

ridge = Ridge(alpha = 5)

# KNN Regressor

knn = KNeighborsRegressor(n_neighbors=5) # n_neighbors is hypparam

# Support Vector Regressor

svr = SVR(kernel='rbf') # non-linear kernel for non-linear data

# Decision Tree Regressor

dt = DecisionTreeRegressor()

# Random Forest regressor

rf = RandomForestRegressor(n_estimators=50) # n_estimators - hyperparam - number of decision trees

# Xgb

xgb = XGBRegressor()

Model Training

• Using xtrain, ytrain data
• Using fit command to train the defined model with xtrain, ytrain

• Linear regression

# Model Training

mlr.fit(xtrain, ytrain)

LinearRegression()

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• modale params

mlr.coef_, mlr.intercept_

(array([ 4.35998650e-01, -1.22526760e+01,  8.78561903e-05,  3.35703585e-03,
        -1.13182228e-01,  8.32390459e-02,  2.65861467e-02, -3.33696057e-03,
        -1.08807587e+00,  1.95642672e+00, -8.65013891e-01]),
 69.88900132095598)

• Total 68 x-coefficients & one intercept

eq = str(mlr.intercept_)

for i,j in zip(xtrain.columns,mlr.coef_):
    mx = '{}*{}'.format(i,j)
    eq = eq+") + ("+mx

eq

'69.88900132095598) + (Delivery_person_Age*0.435998649718888) + (Delivery_person_Ratings*-12.252676012052177) + (distance*8.785619025700895e-05) + (Type_of_order_buffet *0.0033570358454717707) + (Type_of_order_drinks *-0.11318222848594363) + (Type_of_order_meal *0.08323904591986576) + (Type_of_order_snack *0.026586146720603165) + (Type_of_vehicle_bicycle *-0.0033369605706106823) + (Type_of_vehicle_electric_scooter *-1.088075865473514) + (Type_of_vehicle_motorcycle *1.9564267174916001) + (Type_of_vehicle_scooter *-0.8650138914474751'

• Polynomial Regression

• The Dimensionality will become more and will take heavy run time if we take all the inputs

xtrain.head()

	Delivery_person_Age	Delivery_person_Ratings	distance	Type_of_order_buffet	Type_of_order_drinks	Type_of_order_meal	Type_of_order_snack	Type_of_vehicle_bicycle	Type_of_vehicle_electric_scooter	Type_of_vehicle_motorcycle	Type_of_vehicle_scooter
25709	24.0	4.9	10.59	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
27029	28.0	4.5	20.13	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
3625	36.0	5.0	12.41	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0
23660	22.0	4.3	7.71	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
19483	33.0	5.0	6.21	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0

# Considering 5 x columns only for model training, if we consider all the x columns we will have memory error

x_train_p = xtrain.iloc[:,0:5]
x_test_p = xtest.iloc[:,0:5]

x_train_p.head(2)

	Delivery_person_Age	Delivery_person_Ratings	distance	Type_of_order_buffet	Type_of_order_drinks
25709	24.0	4.9	10.59	1.0	0.0
27029	28.0	4.5	20.13	1.0	0.0

# converting x data to poly features

x_train_poly = polyfeat.fit_transform(xtrain)

x_test_poly = polyfeat.transform(xtest)

x_train_poly.shape, x_test_poly.shape

((31914, 78), (13678, 78))

# Model Training

poly.fit(x_train_poly, ytrain)

LinearRegression()

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# params

len(poly.coef_), poly.intercept_

(78, 1226.4641471051107)

• Lasso and Ridge

# Model Training

lasso.fit(xtrain,ytrain), ridge.fit(xtrain,ytrain)

(Lasso(alpha=5), Ridge(alpha=5))

# Model params

lasso.intercept_, lasso.coef_

(16.49438627902628,
 array([ 3.30716643e-01, -0.00000000e+00,  4.83853889e-05, -0.00000000e+00,
        -0.00000000e+00,  0.00000000e+00,  0.00000000e+00, -0.00000000e+00,
        -0.00000000e+00,  0.00000000e+00, -0.00000000e+00]))

ridge.intercept_, ridge.coef_

(69.68896830393777,
 array([ 4.36160966e-01, -1.22107129e+01,  8.81261021e-05,  3.30395858e-03,
        -1.13135987e-01,  8.32786893e-02,  2.65533389e-02, -2.03987678e-03,
        -1.08734414e+00,  1.95544487e+00, -8.66060859e-01]))

• KNN Regressor

# Model Train

knn.fit(xtrain, ytrain)

KNeighborsRegressor()

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# Model params

knn.get_params()

{'algorithm': 'auto',
 'leaf_size': 30,
 'metric': 'minkowski',
 'metric_params': None,
 'n_jobs': None,
 'n_neighbors': 5,
 'p': 2,
 'weights': 'uniform'}

• Support Vector Regression

svr.fit(xtrain, ytrain) # model train

svr.get_params() # params

• Decision Tree Regressor

# Model Training

dt.fit(xtrain, ytrain)

DecisionTreeRegressor()

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dt.get_params()

{'ccp_alpha': 0.0,
 'criterion': 'squared_error',
 'max_depth': None,
 'max_features': None,
 'max_leaf_nodes': None,
 'min_impurity_decrease': 0.0,
 'min_samples_leaf': 1,
 'min_samples_split': 2,
 'min_weight_fraction_leaf': 0.0,
 'random_state': None,
 'splitter': 'best'}

feature_names = list(xtrain.columns)

# Tree Visualization

from sklearn.tree import plot_tree

plt.figure(figsize=(18,18))
plot_tree(dt,filled=True,fontsize=8,feature_names=feature_names,max_depth=5)
plt.show()

• Randomforest Regressor

# Model Train

rf.fit(xtrain, ytrain)

# n_estimators is the number of trees hyperparam

RandomForestRegressor(n_estimators=50)

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xtrain  

	Delivery_person_Age	Delivery_person_Ratings	distance	Type_of_order_buffet	Type_of_order_drinks	Type_of_order_meal	Type_of_order_snack	Type_of_vehicle_bicycle	Type_of_vehicle_electric_scooter	Type_of_vehicle_motorcycle	Type_of_vehicle_scooter
25709	24.0	4.9	10.59	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
27029	28.0	4.5	20.13	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
3625	36.0	5.0	12.41	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0
23660	22.0	4.3	7.71	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
19483	33.0	5.0	6.21	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...
7763	20.0	4.8	12.46	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
15377	27.0	4.7	10.69	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
17730	36.0	4.3	9.18	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0
28030	31.0	5.0	12.42	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
15725	21.0	4.6	20.85	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0

31914 rows × 11 columns

# Model Params

print("Model Params:")
print(rf.get_params())
print()
print("Columns Importance:")
print()
for i, j in zip(rf.feature_names_in_, rf.feature_importances_):
    print(i+": "+str(j))

Model Params:
{'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 50, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}

Columns Importance:

Delivery_person_Age: 0.21113325741176506
Delivery_person_Ratings: 0.2780928396287989
distance: 0.3692709426639769
Type_of_order_buffet : 0.024185862523114998
Type_of_order_drinks : 0.024116206796416385
Type_of_order_meal : 0.02444487452960586
Type_of_order_snack : 0.024032814883678564
Type_of_vehicle_bicycle : 0.0005696476065127236
Type_of_vehicle_electric_scooter : 0.006199652036133369
Type_of_vehicle_motorcycle : 0.03134412591377991
Type_of_vehicle_scooter : 0.006609776006217149

# Tree Visualization

from sklearn.tree import plot_tree

plt.figure(figsize=(18,18))
plot_tree(rf.estimators_[0],filled=True,fontsize=8,feature_names=feature_names,max_depth=5)
plt.show()

• Xgb Regressor

# Model Training

xgb.fit(xtrain, ytrain)

XGBRegressor(base_score=None, booster=None, callbacks=None,
             colsample_bylevel=None, colsample_bynode=None,
             colsample_bytree=None, device=None, early_stopping_rounds=None,
             enable_categorical=False, eval_metric=None, feature_types=None,
             gamma=None, grow_policy=None, importance_type=None,
             interaction_constraints=None, learning_rate=None, max_bin=None,
             max_cat_threshold=None, max_cat_to_onehot=None,
             max_delta_step=None, max_depth=None, max_leaves=None,
             min_child_weight=None, missing=nan, monotone_constraints=None,
             multi_strategy=None, n_estimators=None, n_jobs=None,
             num_parallel_tree=None, random_state=None, ...)

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xgb.get_params()

{'objective': 'reg:squarederror',
 'base_score': None,
 'booster': None,
 'callbacks': None,
 'colsample_bylevel': None,
 'colsample_bynode': None,
 'colsample_bytree': None,
 'device': None,
 'early_stopping_rounds': None,
 'enable_categorical': False,
 'eval_metric': None,
 'feature_types': None,
 'gamma': None,
 'grow_policy': None,
 'importance_type': None,
 'interaction_constraints': None,
 'learning_rate': None,
 'max_bin': None,
 'max_cat_threshold': None,
 'max_cat_to_onehot': None,
 'max_delta_step': None,
 'max_depth': None,
 'max_leaves': None,
 'min_child_weight': None,
 'missing': nan,
 'monotone_constraints': None,
 'multi_strategy': None,
 'n_estimators': None,
 'n_jobs': None,
 'num_parallel_tree': None,
 'random_state': None,
 'reg_alpha': None,
 'reg_lambda': None,
 'sampling_method': None,
 'scale_pos_weight': None,
 'subsample': None,
 'tree_method': None,
 'validate_parameters': None,
 'verbosity': None}

4.5 Model Evaluation/Performance

Back to Top

• Checking Trained Model Performances on Test Data

• Using x_test data we will be getting predictions, these predictions will be compared to y_test

• To check Model Performance we can use evaluation methods

  • Error/Loss
  • Model Score
  • Bias-Variance Trade off (Underfit or Overfit)
  • Cross-Val Score

For regression we can use these evaluation

Performance Metric	Regression
Loss or Error	MeanSquaredError/RMSE (For both Train and Test)
Model Score (Evaluation)	R2score (Coefficient of Determination)
Bias-Variance Trade Off	Higher error & Lower score (underfit)
--	Low Train error & High Test error (Overfit)
Cross-Val Score	Checking trained model performance on entire X and y data

from sklearn.metrics import r2_score, mean_squared_error
from sklearn.model_selection import cross_val_score

generating predictions for xtest data

• Using predict method in trained model to get ypredictions on test data
• Comparing ypred values with ytest for loss and performance

Single Function for all models Evaluation

names = ['Multiple Linear Regression','Polynomial Regression','Lasso Regression',
        'Ridge Regression','KNN Regressor', 'Decision Tree Regressor','RandomForest Regressor','Xgboost Regressor']

models = {'mlr':mlr, 'poly':poly, 'lasso':lasso, 'ridge':ridge,
          'knn':knn, 'dt':dt, 'rf':rf, 'xgb':xgb}

X = pd.concat([xtrain, xtest], axis = 0)
y = pd.concat([ytrain, ytest], axis = 0)

X.shape, y.shape

((45592, 11), (45592,))

# Defining empty lists

trainRMSE = []
testRMSE = []

trainscore = []
testscore = []

fit = []

crossvalscore = []

Short Code

for name, model in models.items():
        
    if name == 'poly':
        
#         ptrain = xtrain.iloc[:,0:5]
#         ptest = xtest.iloc[:,0:5]
        
        pftrain = polyfeat.transform(xtrain)
        pftest = polyfeat.transform(xtest)
        
        # RMSE , R2score
        
        trainRMSE.append(round(np.sqrt(mean_squared_error(ytrain, models[name].predict(pftrain))),2))
        testRMSE.append(round(np.sqrt(mean_squared_error(ytest, models[name].predict(pftest))), 2))
        trainscore.append(round(r2_score(ytrain, models[name].predict(pftrain)),2))
        testscore.append(round(r2_score(ytest, models[name].predict(pftest)),2))
        trscore = r2_score(ytrain, models[name].predict(pftrain))
        tescore = r2_score(ytest, models[name].predict(pftest))
        
        # Bias-Variance Trade off
        
        if trscore>=0.60 and tescore>=0.60:
            if trscore>tescore:
                if trscore-tescore>=0.10:
                    fit.append("Overfit")
                elif trscore-tescore<0.10:
                    fit.append("Goodfit")
                else:
                    fit.append('Nofit')
            elif tescore>trscore:
                if tescore-trscore>=0.10:
                    fit.append("Overfit")
                elif tescore-trscore<0.10:
                    fit.append("Goodfit")
                else:
                    fit.append('Nofit')
                    
        elif trscore<0.60 and tescore<0.60:
            if abs(trscore)==0 and abs(tescore)==0:
                fit.append("Nofit")
            else:
                fit.append("Underfit")
                           
        else:
            fit.append("Nofit")
        
        # Cross validation

        scores = cross_val_score(models[name], X, y, cv=3)
        crossvalscore.append(round(scores.mean(),2))
        
    else:
        
        # RMSE, R2score

        trainRMSE.append(round(np.sqrt(mean_squared_error(ytrain, models[name].predict(xtrain))),2))
        testRMSE.append(round(np.sqrt(mean_squared_error(ytest, models[name].predict(xtest))),2))
        trainscore.append(round(r2_score(ytrain, models[name].predict(xtrain)),2))
        testscore.append(round(r2_score(ytest, models[name].predict(xtest)),2))
        trscore = r2_score(ytrain, models[name].predict(xtrain))
        tescore = r2_score(ytest, models[name].predict(xtest))
        
        # Bias-Variance Trade off
        
        if trscore>=0.60 and tescore>=0.60:
            if trscore>tescore:
                if trscore-tescore>=0.10:
                    fit.append("Overfit")
                elif trscore-tescore<0.10:
                    fit.append("Goodfit")
                else:
                    fit.append('Nofit')
            elif tescore>trscore:
                if tescore-trscore>=0.10:
                    fit.append("Overfit")
                elif tescore-trscore<0.10:
                    fit.append("Goodfit")
                else:
                    fit.append('Nofit')
                    
        elif trscore<0.60 and tescore<0.60:
            if abs(trscore)==0 and abs(tescore)==0:
                fit.append("Nofit")
            else:
                fit.append("Underfit")
                           
        else:
            fit.append("Nofit")

        # Cross-val score
        
        scores = cross_val_score(models[name], X, y, cv=3)
        crossvalscore.append(round(scores.mean(),2))

pd.set_option('display.float_format', lambda x: '%.3f' % x)

display(pd.DataFrame({'Model':names, 'Trainedmodel':models.values(), 'TrainRMSE':trainRMSE, 'TestRMSE':testRMSE,
             'Trainscore':trainscore, 'Testscore':testscore, 'Crossvalscore':crossvalscore, 'Fit':fit}))

	Model	Trainedmodel	TrainRMSE	TestRMSE	Trainscore	Testscore	Crossvalscore	Fit
0	Multiple Linear Regression	LinearRegression()	8.420	8.340	0.190	0.200	0.190	Underfit
1	Polynomial Regression	LinearRegression()	8.100	8.040	0.250	0.260	0.190	Underfit
2	Lasso Regression	Lasso(alpha=5)	8.990	8.970	0.080	0.080	0.080	Underfit
3	Ridge Regression	Ridge(alpha=5)	8.420	8.340	0.190	0.200	0.190	Underfit
4	KNN Regressor	KNeighborsRegressor()	6.870	8.510	0.460	0.170	0.180	Underfit
5	Decision Tree Regressor	DecisionTreeRegressor()	2.170	10.280	0.950	-0.210	-0.220	Nofit
6	RandomForest Regressor	(DecisionTreeRegressor(max_features=1.0, rando...	3.500	8.030	0.860	0.260	0.260	Nofit
7	Xgboost Regressor	XGBRegressor(base_score=None, booster=None, ca...	6.690	7.400	0.490	0.370	0.370	Underfit

X.head(3)

	Delivery_person_Age	Delivery_person_Ratings	distance	Type_of_order_buffet	Type_of_order_drinks	Type_of_order_meal	Type_of_order_snack	Type_of_vehicle_bicycle	Type_of_vehicle_electric_scooter	Type_of_vehicle_motorcycle	Type_of_vehicle_scooter
25709	24.000	4.900	10.590	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000
27029	28.000	4.500	20.130	1.000	0.000	0.000	0.000	0.000	0.000	0.000	1.000
3625	36.000	5.000	12.410	0.000	0.000	0.000	1.000	0.000	0.000	1.000	0.000

Better Perfomance Model

from the above table we can see that Xgboost Regressor Giving less test loss (RMSE) & better test score compared to other.

• On average the error while estimation of price from xgboost is +/- 7 min

4.6 Realtime Prediction

Back to Top

• Using best model (Xgboost) to display flight details & fare for airlines , given data by user

inpdata = pd.read_csv("inpdata.csv")

inpdata.head()

	Delivery_person_Age	Delivery_person_Ratings	Type_of_order	Type_of_vehicle	distance
0	37.000	4.900	snack	motorcycle	3.020
1	34.000	4.500	snack	scooter	20.140
2	23.000	4.400	drinks	motorcycle	1.550
3	38.000	4.700	buffet	motorcycle	7.770
4	32.000	4.600	snack	scooter	6.200

def timeprediction():
    
    print("Model Built on These Below features:")
    print()
    print(list(inpdata.columns[0:len(inpdata.columns)]))
    print()
    print("Enter the below Details one by One:")
    print()
    age = eval(input("Enter Delivery person Age:"))
    rating = eval(input("Enter devlivery person Ratings(out of 5):"))
    print()
    print("Enter Type of Order:")
    display(inpdata.Type_of_order.unique())
    order = input()
    print()
    print("Enter Type of Vehicle:")
    display(inpdata.Type_of_vehicle.unique())
    vehicle = input()
    print()
    distance = eval(input("Enter Distance to be covered in (kms):"))
    print()
    print()
    
    data = pd.DataFrame([[age, rating, order, vehicle, distance]], columns=inpdata.columns[0:len(inpdata.columns)])
    
    print("****************************Given Data**************************")
    display(data)
    
    # Data pre-processing
    
    ohedata = ohe.transform(data[['Type_of_order', 'Type_of_vehicle']]).toarray()
    ohedata = pd.DataFrame(ohedata, columns=ohe.get_feature_names_out())
    data = pd.concat([data.drop(['Type_of_order', 'Type_of_vehicle'], axis=1),ohedata],axis=1)
    
    #predictions
    
    time = round(xgb.predict(data)[0])
    
    print()
    print("Estimated Time for Delivery: {} min".format(time))

timeprediction()

Model Built on These Below features:

['Delivery_person_Age', 'Delivery_person_Ratings', 'Type_of_order', 'Type_of_vehicle', 'distance']

Enter the below Details one by One:

Enter Delivery person Age:23
Enter devlivery person Ratings(out of 5):4.5

Enter Type of Order:

array(['snack ', 'drinks ', 'buffet ', 'meal '], dtype=object)

snack

Enter Type of Vehicle:

array(['motorcycle ', 'scooter ', 'electric_scooter ', 'bicycle '],
      dtype=object)

bicycle

Enter Distance to be covered in (kms):8


****************************Given Data**************************

	Delivery_person_Age	Delivery_person_Ratings	Type_of_order	Type_of_vehicle	distance
0	23	4.500	snack	bicycle	8

Estimated Time for Delivery: 17 min